Data mining of user activity data to identify related items in an electronic catalog

ABSTRACT

Various methods are disclosed for monitoring user browsing activities that indicate user interests in particular products, or other items, represented in an electronic catalog, and for using such information to identify items that are related to one another. In one embodiment, relationships between items within an electronic catalog are determined by identifying items that are frequently viewed by users within the same browsing session (e.g., items A and B are related because a significant portion of those who viewed A also viewed B). The resulting item relatedness data may be stored in a table that maps items to sets of related items. The table may be used to provide personalized item recommendations to users, and/or to supplement item detail pages of the electronic catalog with lists of related items. In one embodiment, the table is used to provide session-specific item recommendations to users.

RELATED APPLICATIONS

This application is a continuation of U.S. application Ser. No.09/821,712, filed Mar. 29, 2001, now U.S. Pat. No. 6,912,505 which is acontinuation-in-part of U.S. application Ser. No. 09/156,237, filed Sep.18, 1998 (now U.S. Pat. No. 6,317,722).

FIELD OF THE INVENTION

The present invention relates to information filtering and data mining.More specifically, the invention relates to methods for determining therelatedness between products or other viewable items represented withina database, and for using item relatedness data to recommend items tousers.

BACKGROUND OF THE INVENTION

A recommendation service is a computer-implemented service thatrecommends items from a database of items. The recommendations arecustomized to particular users based on information known about theusers. One common application for recommendation services involvesrecommending products to online customers. For example, online merchantscommonly provide services for recommending products (books, compactdiscs, videos, etc.) to customers based on profiles that have beendeveloped for such customers. Recommendation services are also commonfor recommending Web sites, articles, and other types of informationalcontent to users.

One technique commonly used by recommendation services is known ascontent-based filtering. Pure content-based systems operate byattempting to identify items which, based on an analysis of itemcontent, are similar to items that are known to be of interest to theuser. For example, a content-based Web site recommendation service mayoperate by parsing the user's favorite Web pages to generate a profileof commonly-occurring terms, and then use this profile to search forother Web pages that include some or all of these terms.

Content-based systems have several significant limitations. For example,content-based methods generally do not provide any mechanism forevaluating the quality or popularity of an item. In addition,content-based methods generally require that the items include some formof content that is amenable to feature extraction algorithms; as aresult, content-based systems tend to be poorly suited for recommendingproducts and other types of items that have little or no useful,parsable content.

Another common recommendation technique is known as collaborativefiltering. In a pure collaborative system, items are recommended tousers based on the interests of a community of users, without anyanalysis of item content. Collaborative systems commonly operate byhaving the users explicitly rate individual items from a list of popularitems. Some systems, such as those described in instead require users tocreate lists of their favorite items. See U.S. Pat. Nos. 5,583,763 and5,749,081. Through this explicit rating or list creating process, eachuser builds a personal profile of his or her preferences. To generaterecommendations for a particular user, the user's profile is compared tothe profiles of other users to identify one or more “similar users.”Items that were rated highly by these similar users, but which have notyet been rated by the user, are then recommended to the user. Animportant benefit of collaborative filtering is that it overcomes theabove-noted deficiencies of content-based filtering.

As with content-based filtering methods, however, existing collaborativefiltering techniques have several problems. One problem is that users ofonline stores frequently do not take the time to explicitly rate theproducts, or create lists of their favorite products. As a result, theonline merchant may be able to provide personalized productrecommendations to only a small segment of its customers.

Further, even if a user takes the time to set up a profile, therecommendations thereafter provided to the user typically will not takeinto account the user's short term shopping or browsing interests. Forexample, the recommendations may not be helpful to a user who ispurchasing a gift for another user, or who is venturing into anunfamiliar product category.

Another problem with collaborative filtering techniques is that an itemin the database normally cannot be recommended until the item has beenrated. As a result, the operator of a new collaborative recommendationsystem is commonly faced with a “cold start” problem in which theservice cannot be brought online in a useful form until a thresholdquantity of ratings data has been collected. In addition, even after theservice has been brought online, it may take months or years before asignificant quantity of the database items can be recommended. Further,as new items are added to the catalog (such as descriptions of newlyreleased products), these new items may not recommendable by the systemfor a period of time.

Another problem with collaborative filtering methods is that the task ofcomparing user profiles tends to be time consuming, particularly if thenumber of users is large (e.g., tens or hundreds of thousands). As aresult, a tradeoff tends to exist between response time and breadth ofanalysis. For example, in a recommendation system that generatesreal-time recommendations in response to requests from users, it may notbe feasible to compare the user's ratings profile to those of all otherusers. A relatively shallow analysis of the available data (leading topoor recommendations) may therefore be performed.

Another problem with both collaborative and content-based systems isthat they generally do not reflect the current preferences of thecommunity of users. In the context of a system that recommends productsto customers, for example, there is typically no mechanism for favoringitems that are currently “hot sellers.” In addition, existing systemstypically do not provide a mechanism for recognizing that the user maybe searching for a particular type or category of item.

SUMMARY

These and other problems are addressed by providing computer-implementedmethods for automatically identifying items that are related to oneanother based on the activities of users. Item relationships aredetermined by analyzing user purchase histories, product viewinghistories, and/or other types of historical browsing data reflectingusers' interests in particular items. This process may be repeatedperiodically (e.g., once per day or once per week) to incorporate thelatest browsing activities of users. The resulting item relatedness datamay be used to provide personalized item recommendations to users (e.g.,product recommendations to customers of an online store), and/or toprovide users with non-personalized lists of related items (e.g., listsof related products on product detail pages).

The present invention also provides methods for recommending items tousers without requiring the users to explicitly rate items or createlists of their favorite items. The personal recommendations arepreferably generated using item relatedness data determined using theabove-mentioned methods, but may be generated using other sources ortypes of item relatedness data (e.g., item relationships determinedusing a content-based analysis). In one embodiment, the personalizedrecommendations are based on the products or other items viewed by thecustomer during a current browsing session, and thus tend to be highlyrelevant to the user's current shopping or browsing purpose.

One aspect of the invention thus involves methods for identifying itemsthat are related to one another. In a preferred embodiment, user actionsthat evidence users' interests in, or affinities for, particular itemsare recorded for subsequent analysis. These item-affinity-evidencingactions may include, for example, the purchase of an item, the viewingof an item's detail page, and/or the addition of an item to an onlineshopping cart. To identify items that are related or “similar” to oneanother, an off-line table generation component analyzes the historiesof item-affinity-evidencing actions of a community of users (preferablyon a periodic basis) to identify correlations between items for whichsuch actions were performed. For example, in one embodiment,user-specific purchase histories are analyzed to identify correlationsbetween item purchases (e.g., products A and B are similar because asignificant number of those who bought A also bought B).

In one embodiment, item viewing activities of users are recorded andanalyzed to identify items that tend to be viewed in combination (e.g.,products A and B are similar because a significant number of those whoviewed A also viewed B during the same browsing session). This may beaccomplished, for example, by maintaining user-specific (and preferablysession-specific) histories of item detail pages viewed by the users.One benefit to using item viewing histories is that the itemrelationships identified include relationships between items that arepure substitutes for each other. This is in contrast to purely purchasebased relationships, which are typically exclusively between items thatare complements of one another (tend to be purchased in combination).

The results of the above process are preferably stored in a table orother data structure that maps items to sets of similar items. Forinstance, for each reference item, the table may store a list of the Nitems deemed most closely related to the reference item. The table alsopreferably stores, for each pair of items, a value indicating thepredicted degree of relatedness between the two items. The table ispreferably generated periodically using a most recent set of purchasehistory data, product viewing history data, and/or other types ofhistorical browsing data reflecting users' item interests. The table maybe used to generate personalized item recommendations, and/or tosupplement item detail pages with lists of related items.

BRIEF DESCRIPTION OF THE DRAWINGS

These and other features of the invention will now be described withreference to the drawings summarized below. These drawings and theassociated description are provided to illustrate specific embodimentsof the invention, and not to limit the scope of the invention.

FIG. 1 illustrates a Web site which implements a recommendation servicewhich operates in accordance with the invention, and illustrates theflow of information between components.

FIG. 2 illustrates a sequence of steps that are performed by therecommendation process of FIG. 1 to generate personalizedrecommendations.

FIG. 3A illustrates one method for generating the similar items tableshown in FIG. 1.

FIG. 3B illustrates another method the generating the similar itemstable of FIG. 1.

FIG. 4 is a Venn diagram illustrating a hypothetical purchase history orviewing history profile of three items.

FIG. 5 illustrates one specific implementation of the sequence of stepsof FIG. 2.

FIG. 6 illustrates the general form of a Web page used to present therecommendations of the FIG. 5 process to the user.

FIG. 7 illustrates another specific implementation of the sequence ofsteps of FIG. 2.

FIG. 8 illustrates components and the data flow of a Web site thatrecords data reflecting product viewing histories of users, and whichuses this data to provide session-based recommendations.

FIG. 9 illustrates the general form of the click stream table in FIG. 8.

FIG. 10 illustrates the general form of a page-item table.

FIG. 11 illustrates one embodiment of a personalized Web page used todisplay session-specific recommendations to a user in the system of FIG.8.

FIG. 12 illustrates the display of viewing-history-based relatedproducts lists on product detail pages.

FIG. 13 illustrates a process for generating the related products listsof the type shown in FIG. 12.

DETAILED DESCRIPTION OF SPECIFIC EMBODIMENTS

The various features and methods will now be described in the context ofa recommendation service, including specific implementations thereof,used to recommend products to users from an online catalog of products.Other features for assisting users in locating products of interest willalso be described.

Throughout the description, the term “product” will be used to refergenerally to both (a) something that may be purchased, and (b) itsrecord or description within a database (e.g., a Sony Walkman and itsdescription within a products database.) A more specific meaning may beimplied by context. The more general term “item” will be used in thesame manner. Although the items in the various embodiments describedbelow are products, it will be recognized that the disclosed methods arealso applicable to other types of items, such as authors, musicalartists, restaurants, chat rooms, other users, and Web sites.

Throughout the description, reference will be made to variousimplementation-specific details, including details of implementations onthe Amazon.com Web site. These details are provided in order to fullyillustrate preferred embodiments of the invention, and not to limit thescope of the invention. The scope of the invention is set forth in theappended claims.

As will be recognized, the various methods set forth herein may beembodied within a wide range of different types of multi-user computersystems, including systems in which information is conveyed to users bysynthesized voice or on wireless devices. Further, as described insection X below, the recommendation methods may be used to recommenditems to users within a physical store (e.g., upon checking out). Thus,it should be understood that the HTML Web site based implementationsdescribed herein illustrate just one type of system in which theinventive methods may be used.

I. Overview of Web Site and Recommendation Services

To facilitate an understanding of the specific embodiments describedbelow, an overview will initially be provided of an example merchant Website in which the various inventive features may be embodied.

As is common in the field of electronic commerce, the merchant Web siteincludes functionality for allowing users to search, browse, and makepurchases from an online catalog of purchasable items or “products,”such as book titles, music titles, video titles, toys, and electronicsproducts. The various product offerings are arranged within a browsetree in which each node represents a category or subcategory of product.Browse nodes at the same level of the tree need not be mutuallyexclusive.

Detailed information about each product can be obtained by accessingthat product's detail page. (As used herein, a “detail page” is a pagethat predominantly contains information about a particular product orother item.) In a preferred embodiment, each product detail pagetypically includes a description, picture, and price of the product,customer reviews of the product, lists of related products, andinformation about the product's availability. The site is preferablyarranged such that, in order to access the detail page of a product, auser ordinarily must either select a link associated with that product(e.g., from a browse node page or search results page) or submit asearch query uniquely identifying the product. Thus, access by a user toa product's detail page generally represents an affirmative request bythe user for information about that product.

Using a shopping cart feature of the site, users can add and removeitems to/from a personal shopping cart which is persistent over multiplesessions. (As used herein, a “shopping cart” is a data structure andassociated code which keeps track of items that have been selected by auser for possible purchase.) For example, a user can modify the contentsof the shopping cart over a period of time, such as one week, and thenproceed to a check out area of the site to purchase the shopping cartcontents.

The user can also create multiple shopping carts within a singleaccount. For example, a user can set up separate shopping carts for workand home, or can set up separate shopping carts for each member of theuser's family. A preferred shopping cart scheme for allowing users toset up and use multiple shopping carts is disclosed in U.S. applicationSer. No. 09/104,942, filed Jun. 25, 1998, titled METHOD AND SYSTEM FORELECTRONIC COMMERCE USING MULTIPLE ROLES, the disclosure of which ishereby incorporated by reference.

The Web site also implements a variety of different recommendationservices for recommending products to users. One such service, known asBookMatcher™, allows users to interactively rate individual books on ascale of 1-5 to create personal item ratings profiles, and appliescollaborative filtering techniques to these profiles to generatepersonal recommendations. The BookMatcher service is described in detailin U.S. Pat. No. 6,064,980, the disclosure of which is herebyincorporated by reference. The site may also include associated servicesthat allow users to rate other types of items, such as CDs and videos.As described below, the ratings data collected by the BookMatcherservice and/or similar services is optionally incorporated into therecommendation processes of the present invention.

Another type of service is a recommendation service which operates inaccordance with the invention. In one embodiment the service(“Recommendation Service”) used to recommend book titles, music titles,video titles, toys, electronics products, and other types of products tousers. The Recommendation Service could also be used in the context ofthe same Web site to recommend other types of items, including authors,artists, and groups or categories of products. Briefly, given a unarylisting of items that are “known” to be of interest to a user (e.g., alist of items purchased, rated, and/or viewed by the user), theRecommendation Service generates a list of additional items(“recommendations”) that are predicted to be of interest to the user.(As used herein, the term “interest” refers generally to a user's likingof or affinity for an item; the term “known” is used to distinguishitems for which the user has implicitly or explicitly indicated somelevel of interest from items predicted by the Recommendation Service tobe of interest.)

The recommendations are generated using a table which maps items tolists of related or “similar” items (“similar items lists”), without theneed for users to rate any items (although ratings data may optionallybe used). For example, if there are three items that are known to be ofinterest to a particular user (such as three items the user recentlypurchased), the service may retrieve the similar items lists for thesethree items from the table, and appropriately combine these lists (asdescribed below) to generate the recommendations.

In accordance with one aspect of the invention, the mappings of items tosimilar items (“item-to-item mappings”) are generated periodically, suchas once per week, from data which reflects the collective interests ofthe community of users. More specifically, the item-to-item mappings aregenerated by an off-line process which identifies correlations betweenknown interests of users in particular items. For example, in oneembodiment described in detail below, the mappings are generating byanalyzing user purchase histories to identify correlations betweenpurchases of particular items (e.g., items A and B are similar because arelatively large portion of the users that purchased item A also boughtitem B). In another embodiment (described in section IV-B below), themappings are generated using histories of the items viewed by individualusers (e.g., items A and B are related because a significant portion ofthose who viewed item A also viewed item B). Item relatedness may alsobe determined based in-whole or in-part on other types of browsingactivities of users (e.g., items A and B are related because asignificant portion of those who put item A in their shopping carts alsoput item B in their shopping carts). Further, the item-to-item mappingscould reflect other types of similarities, including content-basedsimilarities extracted by analyzing item descriptions or content.

An important aspect of the Recommendation Service is that the relativelycomputation-intensive task of correlating item interests is performedoff-line, and the results of this task (item-to-item mappings) arestored in a mapping structure for subsequent look-up. This enables thepersonal recommendations to be generated rapidly and efficiently (suchas in real-time in response to a request by the user), withoutsacrificing breadth of analysis.

In accordance with another aspect of the invention, the similar itemslists read from the table are appropriately weighted (prior to beingcombined) based on indicia of the user's affinity for or currentinterest in the corresponding items of known interest. For example, inone embodiment described below, if the item of known interest waspreviously rated by the user (such as through use of the BookMatcherservice), the rating is used to weight the corresponding similar itemslist. Similarly, the similar items list for a book that was purchased inthe last week may be weighted more heavily than the similar items listfor a book that was purchased four months ago.

Another feature of the invention involves using the current and/orrecent contents of the user's shopping cart as inputs to theRecommendation Service. For example, if the user currently has threeitems in his or her shopping cart, these three items can be treated asthe items of known interest for purposes of generating recommendations,in which case the recommendations may be generated and displayedautomatically when the user views the shopping cart contents. If theuser has multiple shopping carts, the recommendations are preferablygenerated based on the contents of the shopping cart implicitly orexplicitly designated by the user, such as the shopping cart currentlybeing viewed. This method of generating recommendations can also be usedwithin other types of recommendation systems, including content-basedsystems and systems that do not use item-to-item mappings.

Using the current and/or recent shopping cart contents as inputs tendsto produce recommendations that are highly correlated to the currentshort-term interests of the user—even if these short term interests arenot reflected by the user's purchase history. For example, if the useris currently searching for a father's day gift and has selected severalbooks for prospective purchase, this method will have a tendency toidentify other books that are well suited for the gift recipient.

Another feature of the invention involves generating recommendationsthat are specific to a particular shopping cart. This allows a user whohas created multiple shopping carts to conveniently obtainrecommendations that are specific to the role or purpose to theparticular cart. For example, a user who has created a personal shoppingcart for buying books for her children can designate this shopping cartto obtain recommendations of children's books. In one embodiment of thisfeature, the recommendations are generated based solely upon the currentcontents of the shopping cart selected for display. In anotherembodiment, the user may designate one or more shopping carts to be usedto generate the recommendations, and the service then uses the itemsthat were purchased from these shopping carts as the items of knowninterest.

As will be recognized by those skilled in the art, the above-describedtechniques for using shopping cart contents to generate recommendationscan also be incorporated into other types of recommendation systems,including pure content-based systems.

Another feature, which is described in section V-C below, involvesdisplaying session-specific personal recommendations that are based onthe particular items viewed by the user during the current browsingsession. For example, once the user has viewed products A, B and C,these three products may be used as the “items of known interest” forpurposes of generating the session-specific recommendations. Therecommendations are preferably displayed on a special Web page that canselectively be viewed by the user. From this Web page, the user canindividually de-select the viewed items to cause the system to refinethe list of recommended items. The session recommendations may also oralternatively be incorporated into any other type of page, such as thehome page or a shopping cart page.

FIG. 1 illustrates the basic components of the Web site 30, includingthe components used to implement the Recommendation Service. The arrowsin FIG. 1 show the general flow of information that is used by theRecommendation Service. As illustrated by FIG. 1, the Web site 30includes a Web server application 32 (“Web server”) which processes HTTP(Hypertext Transfer Protocol) requests received over the Internet fromuser computers 34. The Web server 32 accesses a database 36 of HTML(Hypertext Markup Language) content which includes product detail pagesand other browsable information about the various products of thecatalog. The “items” that are the subject of the Recommendation Serviceare the titles (preferably regardless of media format such as hardcoveror paperback) and other products that are represented within thisdatabase 36.

The Web site 30 also includes a “user profiles” database 38 which storesaccount-specific information about users of the site. Because a group ofindividuals can share an account, a given “user” from the perspective ofthe Web site may include multiple actual users. As illustrated by FIG.1, the data stored for each user may include one or more of thefollowing types of information (among other things) that can be used togenerate recommendations in accordance with the invention: (a) theuser's purchase history, including dates of purchase, (b) a history ofitems recently viewed by the user, (c) the user's item ratings profile(if any), (d) the current contents of the user's personal shoppingcart(s), and (e) a listing of items that were recently (e.g., within thelast six months) removed from the shopping cart(s) without beingpurchased (“recent shopping cart contents”). If a given user hasmultiple shopping carts, the purchase history for that user may includeinformation about the particular shopping cart used to make eachpurchase; preserving such information allows the Recommendation Serviceto be configured to generate recommendations that are specific to aparticular shopping cart.

As depicted by FIG. 1, the Web server 32 communicates with variousexternal components 40 of the site. These external components 40include, for example, a search engine and associated database (notshown) for enabling users to interactively search the catalog forparticular items. Also included within the external components 40 arevarious order processing modules (not shown) for accepting andprocessing orders, and for updating the purchase histories of the users.

The external components 40 also include a shopping cart process (notshown) which adds and removes items from the users' personal shoppingcarts based on the actions of the respective users. (The term “process”is used herein to refer generally to one or more code modules that areexecuted by a computer system to perform a particular task or set ofrelated tasks.) In one embodiment, the shopping cart processperiodically “prunes” the personal shopping cart listings of items thatare deemed to be dormant, such as items that have not been purchased orviewed by the particular user for a predetermined period of time (e.g.Two weeks). The shopping cart process also preferably generates andmaintains the user-specific listings of recent shopping cart contents.

The external components 40 also include recommendation servicecomponents 44 that are used to implement the site's variousrecommendation services. Recommendations generated by the recommendationservices are returned to the Web server 32, which incorporates therecommendations into personalized Web pages transmitted to users.

The recommendation service components 44 include a BookMatcherapplication 50 which implements the above-described BookMatcher service.Users of the BookMatcher service are provided the opportunity to rateindividual book titles from a list of popular titles. The book titlesare rated according to the following scale:

-   -   1=Bad!    -   2=Not for me    -   3=OK    -   4=Liked it    -   5=Loved it!

Users can also rate book titles during ordinary browsing of the site. Asdepicted in FIG. 1, the BookMatcher application 50 records the ratingswithin the user's items rating profile. For example, if a user of theBookMatcher service gives the book Into Thin Air a score of “5,” theBookMatcher application 50 would record the item (by ISBN or otheridentifier) and the score within the user's item ratings profile. TheBookMatcher application 50 uses the users' item ratings profiles togenerate personal recommendations, which can be requested by the user byselecting an appropriate hyperlink. As described in detail below, theitem ratings profiles are also used by an “Instant Recommendations”implementation of the Recommendation Service.

The recommendation services components 44 also include a recommendationprocess 52, a similar items table 60, and an off-line table generationprocess 66, which collectively implement the Recommendation Service. Asdepicted by the arrows in FIG. 1, the recommendation process 52generates personal recommendations based on information stored withinthe similar items table 60, and based on the items that are known to beof interest (“items of known interest”) to the particular user.

In the embodiments described in detail below, the items of knowninterest are identified based on information stored in the user'sprofile, such as by selecting all items purchased by the user, the itemsrecently viewed by the user, or all items in the user's shopping cart.In other embodiments of the invention, other types of methods or sourcesof information could be used to identify the items of known interest.For example, in a service used to recommend Web sites, the items (Websites) known to be of interest to a user could be identified by parsinga Web server access log and/or by extracting URLs from the “favoriteplaces” list of the user's Web browser. In a service used to recommendrestaurants, the items (restaurants) of known interest could beidentified by parsing the user's credit card records to identifyrestaurants that were visited more than once.

The various processes 50, 52, 66 of the recommendation services may run,for example, on one or more Unix or NT based workstations or physicalservers (not shown) of the Web site 30. The similar items table 60 ispreferably stored as a B-tree data structure to permit efficientlook-up, and may be replicated across multiple machines (together withthe associated code of the recommendation process 52) to accommodateheavy loads.

II. Similar Items Table (FIG. 1)

The general form and content of the similar items table 60 will now bedescribed with reference to FIG. 1. As this table can take on manyalternative forms, the details of the table are intended to illustrate,and not limit, the scope of the invention.

As indicated above, the similar items table 60 maps items to lists ofsimilar items based at least upon the collective interests of thecommunity of users. The similar items table 60 is preferably generatedperiodically (e.g., once per week) by the off-line table generationprocess 66. The table generation process 66 generates the table 60 fromdata that reflects the collective interests of the community of users.In the initial embodiment described in detail herein, the similar itemstable is generated exclusively from the purchase histories of thecommunity of users (as depicted in FIG. 1), and more specifically, byidentifying correlations between purchases of items. In an embodimentdescribed in section IV-B below, the table is generated based on theproduct viewing histories of the community of users, and morespecifically, by identifying correlations between item viewing events.These and other indicia of item relatedness may be appropriatelycombined for purposes of generating the table 60.

Further, in other embodiments, the table 60 may additionally oralternatively be generated from other indicia of user-item interests,including indicia based on users viewing activities, shopping cartactivities, and item rating profiles. For example, the table 60 could bebuilt exclusively from the present and/or recent shopping cart contentsof users (e.g., products A and B are similar because a significantportion of those who put A in their shopping carts also put B in theirshopping carts). The similar items table 60 could also reflectnon-collaborative type item similarities, including content-basedsimilarities derived by comparing item contents or descriptions.

Each entry in the similar items table 60 is preferably in the form of amapping of a popular item 62 to a corresponding list 64 of similar items(“similar items lists”). As used herein, a “popular” item is an itemwhich satisfies some pre-specified popularity criteria. For example, inthe embodiment described herein, an item is treated as popular of it hasbeen purchased by more than 30 customers during the life of the Website. Using this criteria produces a set of popular items (and thus arecommendation service) which grows over time. The similar items list 64for a given popular item 62 may include other popular items.

In other embodiments involving sales of products, the table 60 mayinclude entries for most or all of the products of the online merchant,rather than just the popular items. In the embodiments described herein,several different types of items (books, CDs, videos, etc.) arereflected within the same table 60, although separate tables couldalternatively be generated for each type of item.

Each similar items list 64 consists of the N (e.g., 20) items which,based on correlations between purchases of items, are deemed to be themost closely related to the respective popular item 62. Each item in thesimilar items list 64 is stored together with a commonality index (“CI”)value which indicates the relatedness of that item to the popular item62, based on sales of the respective items. A relatively highcommonality index for a pair of items ITEM A and ITEM B indicates that arelatively large percentage of users who bought ITEM A also bought ITEMB (and vice versa). A relatively low commonality index for ITEM A andITEM B indicates that a relatively small percentage of the users whobought ITEM A also bought ITEM B (and vice versa). As described below,the similar items lists are generated, for each popular item, byselecting the N other items that have the highest commonality indexvalues. Using this method, ITEM A may be included in ITEM B's similaritems list even though ITEM B in not present in ITEM A's similar itemslist.

In the embodiment depicted by FIG. 1, the items are represented withinthe similar items table 60 using product IDs, such as ISBNs or otheridentifiers. Alternatively, the items could be represented within thetable by title ID, where each title ID corresponds to a given “work”regardless of its media format. In either case, different items whichcorrespond to the same work, such as the hardcover and paperbackversions of a given book or the VCR cassette and DVD versions of a givenvideo, are preferably treated as a unit for purposes of generatingrecommendations.

Although the recommendable items in the described system are in the formof book titles, music titles, videos titles, and other types ofproducts, it will be appreciated that the underlying methods and datastructures can be used to recommend a wide range of other types ofitems.

III. General Process for Generating Recommendations Using Similar ItemsTable (FIG. 2)

The general sequence of steps that are performed by the recommendationprocess 52 to generate a set of personal recommendations will now bedescribed with reference to FIG. 2. This process, and the more specificimplementations of the process depicted by FIGS. 5 and 7 (describedbelow), are intended to illustrate, and not limit, the scope of theinvention. Further, as will be recognized, this process may be used incombination with any of the table generation methods described herein(purchase history based, viewing history based, shopping cart based,etc.).

The FIG. 2 process is preferably invoked in real-time in response to anonline action of the user. For example, in an Instant Recommendationsimplementation (FIGS. 5 and 6) of the service, the recommendations aregenerated and displayed in real-time (based on the user's purchasehistory and/or item ratings profile) in response to selection by theuser of a corresponding hyperlink, such as a hyperlink which reads“Instant Book Recommendations” or “Instant Music Recommendations.” In ashopping cart based implementation (FIG. 7), the recommendations aregenerated (based on the user's current and/or recent shopping cartcontents) in real-time when the user initiates a display of a shoppingcart, and are displayed on the same Web page as the shopping cartcontents. In a Session Recommendations implementation (FIGS. 8-11), therecommendations are based on the products (e.g., product detail pages)recently viewed by the user—preferably during the current browsingsession. The Instant Recommendations, shopping cart recommendations, andSession Recommendation embodiments are described below in sections V-A,V-B and V-C, respectively.

Any of a variety of other methods can be used to initiate therecommendations generation process and to display or otherwise conveythe recommendations to the user. For example, the recommendations canautomatically be generated periodically and sent to the user by e-mail,in which case the e-mail listing may contain hyperlinks to the productinformation pages of the recommended items. Further, the personalrecommendations could be generated in advance of any request or actionby the user, and cached by the Web site 30 until requested.

As illustrated by FIG. 2, the first step (step 80) of therecommendations-generation process involves identifying a set of itemsthat are of known interest to the user. The “knowledge” of the user'sinterest can be based on explicit indications of interest (e.g., theuser rated the item highly) or implicit indications of interest (e.g.,the user added the item to a shopping cart or viewed the item). Itemsthat are not “popular items” within the similar items table 60 canoptionally be ignored during this step.

In the embodiment depicted in FIG. 1, the items of known interest areselected from one or more of the following groups: (a) items in theuser's purchase history (optionally limited to those items purchasedfrom a particular shopping cart); (b) items in the user's shopping cart(or a particular shopping cart designated by the user), (c) items ratedby the user (optionally with a score that exceeds a certain threshold,such as two), and (d) items in the “recent shopping cart contents” listassociated with a given user or shopping cart. In other embodiments, theitems of known interest may additionally or alternatively be selectedbased on the viewing activities of the user. For example, therecommendations process 52 could select items that were viewed by theuser for an extended period of time, viewed more than once, or viewedduring the current session. Further, the user could be prompted toselect items of interest from a list of popular items.

For each item of known interest, the service retrieves the correspondingsimilar items list 64 from the similar items table 60 (step 82), if sucha list exists. If no entries exist in the table 60 for any of the itemsof known interest, the process 52 may be terminated; alternatively, theprocess could attempt to identify additional items of interest, such asby accessing other sources of interest information.

In step 84, the similar items lists 64 are optionally weighted based oninformation about the user's affinity for the corresponding items ofknown interest. For example, a similar items list 64 may be weightedheavily if the user gave the corresponding popular item a rating of “5”on a scale or 1-5, or if the user purchased multiple copies of the item.Weighting a similar items list 64 heavily has the effect of increasingthe likelihood that the items in that list we be included in therecommendations ultimately presented to the user. In one implementationdescribed below, the user is presumed to have a greater affinity forrecently purchased items over earlier purchased items. Similarly, whereviewing histories are used to identify items of interest, items viewedrecently may be weighted more heavily than earlier viewed items.

The similar items lists 64 are preferably weighted by multiplying thecommonality index values of the list by a weighting value. Thecommonality index values as weighted by any applicable weighting valueare referred to herein as “scores.” In some embodiments, therecommendations may be generated without weighting the similar itemslists 64 (as in the Shopping Cart recommendations implementationdescribed below).

If multiple similar items lists 64 are retrieved in step 82, the listsare appropriately combined (step 86), preferably by merging the listswhile summing or otherwise combining the scores of like items. Theresulting list is then sorted (step 88) in order of highest-to-lowestscore. By combining scores of like items, the process takes intoconsideration whether an item is similar to more than one of the itemsof known interest. For example, an item that is related to two or moreof the items of known interest will generally be ranked more highly than(and thus recommended over) an item that is related to only one of theitems of known interest. In another embodiment, the similar items listsare combined by taking their intersection, so that only those items thatare similar to all of the items of known interest are retained forpotential recommendation to the user.

In step 90, the sorted list is preferably filtered to remove unwanteditems. The items removed during the filtering process may include, forexample, items that have already been purchased or rated by the user,and items that fall outside any product group (such as music or books),product category (such as non-fiction), or content rating (such as PG oradult) designated by the user. The filtering step could alternatively beperformed at a different stage of the process, such as during theretrieval of the similar items lists from the table 60. The result ofstep 90 is a list (“recommendations list”) of other items to berecommended to the user.

In step 92, one or more additional items are optionally added to therecommendations list. In one embodiment, the items added in step 92 areselected from the set of items (if any) in the user's “recent shoppingcart contents” list. As an important benefit of this step, therecommendations include one or more items that the user previouslyconsidered purchasing but did not purchase. The items added in step 92may additionally or alternatively be selected using anotherrecommendations method, such as a content-based method.

Finally, in step 94, a list of the top M (e.g., 15) items of therecommendations list are returned to the Web server 32 (FIG. 1). The Webserver incorporates this list into one or more Web pages that arereturned to the user, with each recommended item being presented as ahypertextual link to the item's product information page. Therecommendations may alternatively be conveyed to the user by email,facsimile, or other transmission method. Further, the recommendationscould be presented as advertisements for the recommended items.

IV. Generation of Similar Items Table (FIGS. 3 and 4)

The table-generation process 66 is preferably executed periodically(e.g., once a week) to generate a similar items table 60 that reflectsthe most recent purchase history data (FIG. 3A), the most recent productviewing history data (FIG. 3B), and/or other types of browsingactivities that reflect item interests of users. The recommendationprocess 52 uses the most recently generated version of the table 60 togenerate recommendations.

IV-A. Use of Purchase Histories to Identify Related Items (FIG. 3A)

FIG. 3A illustrates the sequence of steps that are performed by thetable generation process 66 to build the similar items table 60 usingpurchase history data. An item-viewing-history based embodiment of theprocess is depicted in FIG. 3B and is described separately below. Thegeneral form of temporary data structures that are generated during theprocess are shown at the right of the drawing. As will be appreciated bythose skilled in the art, any of a variety of alternative methods couldbe used to generate the table 60.

As depicted by FIG. 3A, the process initially retrieves the purchasehistories for all customers (step 100). Each purchase history is in thegeneral form of the user ID of a customer together with a list of theproduct IDs (ISBNs, etc.) of the items (books, CDs, videos, etc.)purchased by that customer. In embodiments which support multipleshopping carts within a given account, each shopping cart could betreated as a separate customer for purposes of generating the table. Forexample, if a given user (or group of users that share an account)purchased items from two different shopping carts within the sameaccount, these purchases could be treated as the purchases of separateusers.

The product IDs may be converted to title IDs during this process, orwhen the table 60 is later used to generate recommendations, so thatdifferent versions of an item (e.g., hardcover and paperback) arerepresented as a single item. This may be accomplished, for example, byusing a separate database which maps product IDs to title IDs. Togenerate a similar items table that strongly reflects the current tastesof the community, the purchase histories retrieved in step 100 can belimited to a specific time period, such as the last six months.

In steps 102 and 104, the process generates two temporary tables 102Aand 104A. The first table 102A maps individual customers to the itemsthey purchased. The second table 104A maps items to the customers thatpurchased such items. To avoid the effects of “ballot stuffing,”multiple copies of the same item purchased by a single customer arerepresented with a single table entry. For example, even if a singlecustomer purchased 4000 copies of one book, the customer will be treatedas having purchased only a single copy. In addition, items that weresold to an insignificant number (e.g., <15) of customers are preferablyomitted or deleted from the tables 102A, 104B.

In step 106, the process identifies the items that constitute “popular”items. This may be accomplished, for example, by selecting from theitem-to-customers table 104A those items that were purchased by morethan a threshold number (e.g., 30) of customers. In the context of amerchant Web site such as that of Amazon.com, Inc., the resulting set ofpopular items may contain hundreds of thousands or millions of items.

In step 108, the process counts, for each (popular_item, other_item)pair, the number of customers that are in common. A pseudocode sequencefor performing this step is listed in Table 1. The result of step 108 isa table that indicates, for each (popular_item, other_item) pair, thenumber of customers the two have in common. For example, in thehypothetical table 108A of FIG. 3A, POPULAR_A and ITEM_B have seventycustomers in common, indicating that seventy customers bought bothitems.

TABLE 1 for each popular_item  for each customer in customers of item  for each other_item in items of customer    incrementcommon-customer-count(popular_item, other_item)

In step 110, the process generates the commonality indexes for each(popular_item, other_item) pair in the table 108A. As indicated above,the commonality index (CI) values are measures of the similarity betweentwo items, with larger CI values indicating greater degrees ofsimilarity. The commonality indexes are preferably generated such that,for a given popular_item, the respective commonality indexes of thecorresponding other_items take into consideration both (a) the number ofcustomers that are common to both items, and (b) the total number ofcustomers of the other_item. A preferred method for generating thecommonality index values is set forth in equation (1) below, whereN_(common) is the number of users who purchased both A and B, sqrt is asquare-root operation, N_(A) is the number of users who purchased A, andN_(B) is the number of users who purchased B.CI(item_(—) A,item_(—) B)=N _(common)/sqrt(N _(A) ×N _(B))  Equation (1)

FIG. 4 illustrates this method in example form. In the FIG. 4 example,item_P (a popular item) has two “other items,” item_X and item_Y. Item_Phas been purchased by 300 customers, item_X by 300 customers, and item_Yby 30,000 customers. In addition, item_P and item_X have 20 customers incommon, and item_P and item_Y have 25 customers in common. Applying theequation above to the values shown in FIG. 4 produces the followingresults:CI(item_(—) P,item_(—) X)=20/sqrt(300×300))=0.0667CI(item_(—) P,item_(—) Y)=25/sqrt(300×30,000))=0.0083Thus, even though items P and Y have more customers in common than itemsP and X, items P and X are treated as being more similar than items Pand Y. This result desirably reflects the fact that the percentage ofitem_X customers that bought item_P (6.7%) is much greater than thepercentage of item_Y customers that bought item_P (0.08%).

Because this equation is symmetrical (i.e., CI(item_A,item_B)=CI(item_B, item_A)), it is not necessary to separately calculatethe CI value for every location in the table 108A. In other embodiments,an asymmetrical method may be used to generate the CI values. Forexample, the CI value for a (popular_item, other_item) pair could begenerated as (customers of popular_item and other_item)/(customers ofother_item).

Following step 110 of FIG. 3A, each popular item has a respective“other_items” list which includes all of the other_items from the table108A and their associated CI values. In step 112, each other_items listis sorted from highest-to-lowest commonality index. Using the FIG. 4values as an example, item_X would be positioned closer to the top ofthe item_B's list than item_Y, since 0.014907>0.001643.

In step 114, the sorted other_items lists are filtered by deleting alllist entries that have fewer than 3 customers in common. For example, inthe other_items list for POPULAR_A in table 108A, ITEM_A would bedeleted since POPULAR_A and ITEM_A have only two customers in common.Deleting such entries tends to reduce statistically poor correlationsbetween item sales. In step 116, the sorted other_items lists aretruncated to length N to generate the similar items lists, and thesimilar items lists are stored in a B-tree table structure for efficientlook-up.

IV-B. Use of Product Viewing Histories to Identify Related Items (FIG.3B)

One limitation with the process of FIG. 3A is that it is not well suitedfor determining the similarity or relatedness between products for whichlittle or no purchase history data exists. This problem may arise, forexample, when the online merchant adds new products to the onlinecatalog, or carries expensive or obscure products that are infrequentlysold. The problem also arises in the context of online systems thatmerely provide information about products without providing an optionfor users to purchase the products (e.g., the Web site of ConsumerReports).

Another limitation is that the purchase-history based method isgenerally incapable of identifying relationships between items that aresubstitutes for (purchased in place of) each other. Rather, theidentified relationships tend to be exclusively between items that arecomplements (i.e., one is purchased in addition to the other).

In accordance with one aspect of the invention, these limitations areovercome by incorporating user-specific (and preferablysession-specific) product viewing histories into the process ofdetermining product relatedness. Specifically, the Web site system isdesigned to store user click stream or query log data reflecting theproducts viewed by each user during ordinary browsing of the onlinecatalog. This may be accomplished, for example, by recording the productdetail pages viewed by each user. Products viewed on other areas of thesite, such as on search results pages and browse node pages, may also beincorporated into the users' product viewing histories.

During generation of the similar items table 60, the user-specificviewing histories are analyzed, preferably using a similar process tothat used to analyze purchase history data (FIG. 3A), as an additionalor an alternative measure of product similarity. For instance, if arelatively large percentage of the users who viewed product A alsoviewed product B, products A and B may be deemed sufficiently related tobe included in each other's similar items lists. The product viewinghistories may be analyzed on a per session basis (i.e., only take intoaccount those products viewed during the same session), or on amulti-session basis (e.g., take into consideration co-occurrences ofproducts within the entire recorded viewing of browsing history of eachuser). Other known metrics of product similarity, such as those based onuser purchase histories or a content based analysis, may be incorporatedinto the same process to improve reliability.

An important benefit to incorporating item viewing histories into theitem-to-item mapping process is that relationships can be determinedbetween items for which little or no purchase history data exists (e.g.,an obscure product or a newly released product). As a result,relationships can typically be identified between a far greater range ofitems than is possible with a pure purchase-based approach.

Another important benefit to using viewing histories is that the itemrelationships identified include relationships between items that arepure substitutes. For example, the purchase-based item-to-itemsimilarity mappings ordinarily would not map one large-screen TV toanother large-screen TV, since it is rare that a single customer wouldpurchase more than one large-screen TV. On the other hand, a mappingthat reflects viewing histories would likely link two large-screen TVstogether since it is common for a customer to visit the detail pages ofmultiple large-screen TVs during the same browsing session.

The query log data used to implement this feature may optionallyincorporate browsing activities over multiple Web sites (e.g., the Websites of multiple, affiliated merchants). Such multi-site query log datamay be obtained using any of a variety of methods. One known method isto have the operator of Web site A incorporate into a Web page of Website A an object served by Web site B (e.g., a small graphic). With thismethod, any time a user accesses this Web page (causing the object to berequested from Web site B), Web site B can record the browsing event.Another known method for collecting multi-site query log data is to haveusers download a browser plug-in, such as the plug-in provided by AlexaInternet Inc., that reports browsing activities of users to a centralserver. The central server then stores the reported browsing activitiesas query log data records. Further, the entity responsible forgenerating the similar items table could obtain user query log datathrough contracts with ISPs, merchants, or other third party entitiesthat provide Web sites for user browsing.

Although the term “viewing” is used herein to refer to the act ofaccessing product information, it should be understood that the userdoes not necessarily have to view the information about the product.Specifically, some merchants support the ability for users to browsetheir electronic catalogs by voice. For example, in some systems, userscan access voiceXML versions of the site's Web pages using a telephoneconnection to a voice recognition and synthesis system. In such systems,a user request for voice-based information about a product may betreated as a product viewing event.

FIG. 3B illustrates a preferred process for generating the similar itemstable 60 (FIG. 1) from query log data reflecting product viewing events.Methods that may be used to capture the query log data, and identifyproduct viewing events therefrom, are described separately below insection V-C. As will be apparent, the embodiments of FIGS. 3A and 3B canbe appropriately combined such that the similarities reflected in thesimilar items table 60 incorporate both correlations in item purchasesand correlations in item viewing events.

As depicted by FIG. 3B, the process initially retrieves the query logrecords for all browsing sessions (step 300). In one embodiment, onlythose query log records that indicate sufficient viewing activity (suchas more than 5 items viewed in a browsing session) are retrieved. Inthis embodiment, some of the query log records may correspond todifferent sessions by the same user. Preferably, the query log recordsof many thousands of different users are used to build the similar itemstable 60.

Each query log record is preferably in the general form of a browsingsession identification together with a list of the identifiers of theitems viewed in that browsing session. The item IDs may be converted totitle IDs during this process, or when the table 60 is later used togenerate recommendations, so that different versions of an item arerepresented as a single item. Each query log record may alternativelylist some or all of the pages viewed during the session, in which case alook up table may be used to convert page IDs to item or product IDs.

In steps 302 and 304, the process builds two temporary tables 302A and304A. The first table 302A maps browsing sessions to the items viewed inthe sessions. A table of the type shown in FIG. 9 (discussed separatelybelow) may be used for this purpose. Items that were viewed within aninsignificant number (e.g., <15) of browsing sessions are preferablyomitted or deleted from the tables 302A and 304A. In one embodiment,items that were viewed multiple times within a browsing session arecounted as items viewed once within a browsing session.

In step 306, the process identifies the items that constitute “popular”items. This may be accomplished, for example, by selecting from table304A those items that were viewed within more than a threshold number(e.g., 30) of sessions. In the context of a Web site of a typical onlinemerchant that sells many thousands or millions of different items, thenumber of popular items in this embodiment will desirably be far greaterthan in the purchase-history-based embodiment of FIG. 3A. As a result,similar items lists 64 can be generated for a much greater portion ofthe items in the online catalog—including items for which little or nosales data exists.

In step 308, the process counts, for each (popular_item, other_item)pair, the number of sessions that are in common. A pseudocode sequencefor performing this step is listed in Table 2. The result of step 308 isa table that indicates, for each (popular_item, other_item) pair, thenumber of sessions the two have in common. For example, in thehypothetical table 308A of FIG. 3B, POPULAR_A and ITEM_B have seventysessions in common, indicating that in seventy sessions both items wereviewed.

TABLE 2 for each popular_item  for each session in sessions ofpopular_item   for each other_item in items of session    incrementcommon-session-count(popular_item, other_item)

In step 310, the process generates the commonality indexes for each(popular_item, other_item) pair in the table 308A. The commonality index(CI) values are measures of the similarity or relatedness between twoitems, with larger CI values indicating greater degrees of similarity.The commonality indexes are preferably generated such that, for a givenpopular_item, the respective commonality indexes of the correspondingother_items take into consideration the following (a) the number ofsessions that are common to both items (i.e, sessions in which bothitems were viewed), (b) the total number of sessions in which theother_item was viewed, and (c) the number of sessions in which thepopular_item was viewed. Equation (1), discussed above, may be used forthis purpose, but with the variables redefined as follows: N_(common) isthe number of sessions in which both A and B were viewed, N_(A) is thenumber of sessions in which A was viewed, and N_(B) is the number ofsessions in which B was viewed. Other calculations that reflect thefrequency with which A and B co-occur within the product viewinghistories may alternatively be used.

FIG. 4 illustrates this method in example form. In the FIG. 4 example,item_P (a popular item) has two “other items,” item_X and item_Y. Item_Phas been viewed in 300 sessions, item_X in 300 sessions, and item_Y in30,000 sessions. In addition, item_P and item_X have 20 sessions incommon, and item_P and item_Y have 25 sessions in common. Applying theequation above to the values shown in FIG. 4 produces the followingresults:CI(item_(—) P,item_(—) X)=20/sqrt(300×300))=0.0667CI(item_(—) P,item_(—) Y)=25/sqrt(300×30,000))=0.0083Thus, even though items P and Y have more sessions in common than itemsP and X, items P and X are treated as being more similar than items Pand Y. This result desirably reflects the fact that the percentage ofitem_X sessions in which item_P was viewed (6.7%) is much greater thanthe percentage of item_Y sessions in which item_P was viewed (0.08%).

Because this equation is symmetrical (i.e., CI(item_A,item_B)=CI(item_B, item_A)), it is not necessary to separately calculatethe CI value for every location in the table 308A. As indicated above,an asymmetrical method may alternatively be used to generate the CIvalues.

Following step 310 of FIG. 3B, each popular item has a respective“other_items” list which includes all of the other items from the table308A and their associated CI values. In step 312, each other_items listis sorted from highest-to-lowest commonality index. Using the FIG. 4values as an example, item_X would be positioned closer to the top ofthe item_B's list than item_Y, since 0.014907>0.001643. In step 314, thesorted other_items lists are filtered by deleting all list entries thathave fewer than a threshold number of sessions in common (e.g., 3sessions).

In one embodiment, the items in the other_items list are weighted tofavor some items over others. For example, items that are new releasesmay be weighted more heavily than older items. For items in theother_items list of a popular item, their CI values are preferablymultiplied by the corresponding weights. Therefore, the more heavilyweighted items (such as new releases) are more likely to be consideredrelated and more likely to be recommended to users.

In step 316, the sorted other_items lists are truncated to length N(e.g., 20) to generate the similar items lists, and the similar itemslists are stored in a B-tree table structure for efficient look-up.

One variation of the method shown in FIG. 3B is to use multiple-sessionviewing histories of users (e.g., the entire viewing history of eachuser) in place of the session-specific product viewing histories. Thismay be accomplished, for example, by combining the query log datacollected from multiple browsing sessions of the same user, and treatingthis data as one “session” for purposes of the FIG. 3B process. Withthis variation, the similarity between a pair of items, A and B,reflects whether a large percentage of the users who viewed A alsoviewed B—during either the same session or a different session.

Another variation is to use the “distance” between two product viewingevents as an additional indicator of product relatedness. For example,if a user views product A and then immediately views product B, this maybe treated as a stronger indication that A and B are related than if theuser merely viewed A and B during the same session. The distance may bemeasured using any appropriate parameter that can be recorded within asession record, such as time between product viewing events, number ofpage accesses between product viewing events, and/or number of otherproducts viewed between product viewing events. Distance may also beincorporated into the purchase based method of FIG. 3A.

As with generation of the purchase-history-based similar items table,the viewing-history-based similar items table is preferably generatedperiodically, such as once per day or once per week, using an off-lineprocess. Each time the table 60 is regenerated, query log data recordedsince the table was last generated is incorporated into theprocess—either alone or in combination with previously-recorded querylog data. For example, the temporary tables 302A and 304A of FIG. 3B maybe saved from the last table generation event and updated with new querylog data to complete the process of FIG. 3B.

IV-C. Determination of Item Relatedness Using Other Types of UserActivities

The process flows shown in FIGS. 3A and 3B differ primarily in that theyuse different types of user actions as evidence of users' interests in aparticular items. In the method shown in FIG. 3A, a user is assumed tobe interested in an item if the user purchased the item; and in theprocess shown in 3B, a user is assumed to be interested in an item ifthe user viewed the item. Any of a variety of other types of useractions that evidence a user's interest in a particular item mayadditionally or alternatively be used, alone or in combination, togenerate the similar items table 60. The following are examples of othertypes of user actions that may be used for this purpose.

-   -   (1) Placing an item in a personal shopping cart. With this        method, products A and B may be treated as similar if a large        percentage of those who put A in an online shopping cart also        put B in the shopping cart. As with product viewing histories,        the shopping cart contents histories of users may be evaluated        on a per session basis (i.e., only consider items placed in the        shopping cart during the same session), on a multiple-session        basis (e.g., consider the entire shopping cart contents history        of each user as a unit), or using another appropriate method        (e.g., only consider items that were in the shopping cart at the        same time).    -   (2) Placing a bid on an item in an online auction. With this        method, products A and B may be treated as related if a large        percentage of those who placed a bid on A also placed a bid        on B. The bid histories of users may be evaluated on a per        session basis or on a multiple-session basis. The table        generated by this process may, for example, be used to recommend        related auctions, and/or related retail items, to users who view        auction pages.    -   (3) Placing an item on a wish list. With this method, products A        and B may be treated as related if a large percentage of those        who placed A on their respective electronic wish lists (or other        gift registries) also placed B on their wish lists.    -   (4) Submitting a favorable review for an item. With this method,        products A and B may be treated as related if a large percentage        of those who favorably reviewed A also favorably reviewed B. A        favorable review may be defined as a score that satisfies a        particular threshold (e.g., 4 or above on a scale of 1-5).    -   (5) Purchasing an item as a gift for someone else. With this        method, products A and B may be treated as related if a large        percentage of those who purchased A as a gift also purchased B        as a gift. This could be especially helpful during the holidays        to help customers find more appropriate gifts based on the        gift(s) they've already bought.

With the above and other types of item-affinity-evidencing actions,equation (1) above may be used to generate the CI values, with thevariables of equation (1) generalized as follows:

-   -   N_(common) is the number of users that performed the        item-affinity-evidencing action with respect to both item A and        item B during the relevant period (browsing session, entire        browsing history, etc.);    -   N_(A) is the number of users who performed the action with        respect to item A during the relevant period; and    -   N_(B) is the number of users who performed the action with        respect to item B during the relevant period.

As indicated above, any of a variety non-user-action-based methods forevaluating similarities between items could be incorporated into thetable generation process 66. For example, the table generation processcould compare item contents and/or use previously-assigned productcategorizations as additional or alternative indicators of itemrelatedness. An important benefit of the user-action-based methods(e.g., of FIGS. 3A and 3B), however, is that the items need not containany content that is amenable to feature extraction techniques, and neednot be pre-assigned to any categories. For example, the method can beused to generate a similar items table given nothing more than theproduct IDs of a set of products and user purchase histories and/orviewing histories with respect to these products.

Another important benefit of the Recommendation Service is that the bulkof the processing (the generation of the similar items table 60) isperformed by an off-line process. Once this table has been generated,personalized recommendations can be generated rapidly and efficiently,without sacrificing breadth of analysis.

V. Example Uses of Similar Items Table to Generate PersonalRecommentations

Three specific implementations of the Recommendation Service, referredto herein as Instant Recommendations, Shopping Basket Recommendations,and Session Recommendations, will now be described in detail. Thesethree implementations differ in that each uses a different source ofinformation to identify the “items of known interest” of the user whoserecommendations are being generated. In all three implementations, therecommendations are preferably generated and displayed substantially inreal time in response to an action by the user.

Any of the methods described above may be used to generate the similaritems tables 60 used in these three service implementations. Further,all three (and other) implementations may be used within the same Website or other system, and may share the same similar items table 60.

V-A Instant Recommendations Service (FIGS. 5 and 6)

A specific implementation of the Recommendation Service, referred toherein as the Instant Recommendations service, will now be describedwith reference to FIGS. 5 and 6.

As indicated above, the Instant Recommendations service is invoked bythe user by selecting a corresponding hyperlink from a Web page. Forexample, the user may select an “Instant Book Recommendations” orsimilar hyperlink to obtain a listing of recommended book titles, or mayselect a “Instant Music Recommendations” or “Instant VideoRecommendations” hyperlink to obtain a listing of recommended music orvideo titles. As described below, the user can also request that therecommendations be limited to a particular item category, such as“non-fiction,” “jazz” or “comedies.” The “items of known interest” ofthe user are identified exclusively from the purchase history and anyitem ratings profile of the particular user. The service becomesavailable to the user (i.e., the appropriate hyperlink is presented tothe user) once the user has purchased and/or rated a threshold number(e.g. three) of popular items within the corresponding product group. Ifthe user has established multiple shopping carts, the user may also bepresented the option of designating a particular shopping cart to beused in generating the recommendations.

FIG. 5 illustrates the sequence of steps that are performed by theInstant Recommendations service to generate personal recommendations.Steps 180-194 in FIG. 5 correspond, respectively, to steps 80-94 in FIG.2. In step 180, the process 52 identifies all popular items that havebeen purchased by the user (from a particular shopping cart, ifdesignated) or rated by the user, within the last six months. In step182, the process retrieves the similar items lists 64 for these popularitems from the similar items table 60.

In step 184, the process 52 weights each similar items list based on theduration since the associated popular item was purchased by the user(with recently-purchased items weighted more heavily), or if the popularitem was not purchased, the rating given to the popular item by theuser. The formula used to generate the weight values to apply to eachsimilar items list is listed in C in Table 2. In this formula,“is_purchased” is a boolean variable which indicates whether the popularitem was purchased, “rating” is the rating value (1-5), if any, assignedto the popular item by the user, “order_date” is the date/time (measuredin seconds since 1970) the popular item was purchased, “now” is thecurrent date/time (measured in seconds since 1970), and “6 months” issix months in seconds.

TABLE 2 1 Weight = ( (is_purchased ? 5 : rating) * 2 − 5) * 2 ( 1 +(max( (is purchased ? order_date : 0) − (now − 6 months), 0 ) ) 3 / (6months))

In line 1 of the formula, if the popular item was purchased, the value“5” (the maximum possible rating value) is selected; otherwise, theuser's rating of the item is selected. The selected value (which mayrange from 1-5) is then multiplied by 2, and 5 is subtracted from theresult. The value calculated in line 1 thus ranges from a minimum of −3(if the item was rated a “1”) to a maximum of 5 (if the item waspurchased or was rated a “5”).

The value calculated in line 1 is multiplied by the value calculated inlines 2 and 3, which can range from a minimum of 1 (if the item waseither not purchased or was purchased at least six months ago) to amaximum of 2 (if order_date=now). Thus, the weight can range from aminimum of −6 to a maximum of 10. Weights of zero and below indicatethat the user rated the item a “2” or below. Weights higher than 5indicate that the user actually purchased the item (although a weight of5 or less is possible even if the item was purchased), with highervalues indicating more recent purchases.

The similar items lists 64 are weighted in step 184 by multiplying theCI values of the list by the corresponding weight value. For example, ifthe weight value for a given popular item is ten, and the similar itemslist 64 for the popular item is

(productid_A, 0.10), (productid_B, 0.09), (productid_C, 0.08), . . .

the weighted similar items list would be:

(productid_A, 1.0), (productid_B, 0.9), (productid_C, 0.8), . . . .

The numerical values in the weighted similar items lists are referred toas “scores.”

In step 186, the weighted similar items lists are merged (if multiplelists exist) to form a single list. During this step, the scores of likeitems are summed. For example, if a given other_item appears in threedifferent similar items lists 64, the three scores (including anynegative scores) are summed to produce a composite score.

In step 188, the resulting list is sorted from highest-to-lowest score.The effect of the sorting operation is to place the most relevant itemsat the top of the list. In step 190, the list is filtered by deletingany items that (1) have already been purchased or rated by the user, (2)have a negative score, or (3) do not fall within the designated productgroup (e.g., books) or category (e.g., “science fiction,” or “jazz”).

In step 192 one or more items are optionally selected from the recentshopping cart contents list (if such a list exists) for the user,excluding items that have been rated by the user or which fall outsidethe designated product group or category. The selected items, if any,are inserted at randomly-selected locations within the top M (e.g., 15)positions in the recommendations list. Finally, in step 194, the top Mitems from the recommendations list are returned to the Web server 32,which incorporates these recommendations into one or more Web pages.

The general form of such a Web page is shown in FIG. 6, which lists fiverecommended items. From this page, the user can select a link associatedwith one of the recommended items to view the product information pagefor that item. In addition, the user can select a “more recommendations”button 200 to view additional items from the list of M items. Further,the user can select a “refine your recommendations” link to rate orindicate ownership of the recommended items. Indicating ownership of anitem causes the item to be added to the user's purchase history listing.

The user can also select a specific category such as “non-fiction” or“romance” from a drop-down menu 202 to request category-specificrecommendations. Designating a specific category causes items in allother categories to be filtered out in step 190 (FIG. 5).

V-B Shopping Cart Based Recommendations (FIG. 7)

Another specific implementation of the Recommendation Service, referredto herein as Shopping Cart recommendations, will now be described withreference to FIG. 7.

The Shopping Cart recommendations service is preferably invokedautomatically when the user displays the contents of a shopping cartthat contains more than a threshold number (e.g., 1) of popular items.The service generates the recommendations based exclusively on thecurrent contents of the shopping cart (i.e., only the shopping cartcontents are used as the “items of known interest”). As a result, therecommendations tend to be highly correlated to the user's currentshopping interests. In other implementations, the recommendations mayalso be based on other items that are deemed to be of current interestto the user, such as items in the recent shopping cart contents of theuser and/or items recently viewed by the user. Further, otherindications of the user's current shopping interests could beincorporated into the process. For example, any search terms typed intothe site's search engine during the user's browsing session could becaptured and used to perform content-based filtering of the recommendeditems list.

FIG. 7 illustrates the sequence of steps that are performed by theShopping Cart recommendations service to generate a set ofshopping-cart-based recommendations. In step 282, the similar items listfor each popular item in the shopping cart is retrieved from the similaritems table 60. The similar items list for one or more additional itemsthat are deemed to be of current interest could also be retrieved duringthis step, such as the list for an item recently deleted from theshopping cart or recently viewed for an extended period of time.

In step 286, these similar items lists are merged while summing thecommonality index (CI) values of like items. In step 288, the resultinglist is sorted from highest-to-lowest score. In step 290, the list isfiltered to remove any items that exist in the shopping cart or havebeen purchased or rated by the user. Finally, in step 294, the top M(e.g., 5) items of the list are returned as recommendations. Therecommendations are preferably presented to the user on the same Webpage (not shown) as the shopping cart contents. An importantcharacteristic of this process is that the recommended products tend tobe products that are similar to more than one of the products in theshopping cart (since the CI values of like items are combined). Thus, ifthe items in the shopping cart share some common theme orcharacteristic, the items recommended to the user will tend to have thissame theme or characteristic.

If the user has defined multiple shopping carts, the recommendationsgenerated by the FIG. 7 process may be based solely on the contents ofthe shopping cart currently selected for display. As described above,this allows the user to obtain recommendations that correspond to therole or purpose of a particular shopping cart (e.g., work versus home).

The various uses of shopping cart contents to generate recommendationsas described above can be applied to other types of recommendationsystems, including content-based systems. For example, the currentand/or past contents of a shopping cart can be used to generaterecommendations in a system in which mappings of items to lists ofsimilar items are generated from a computer-based comparison of itemcontents. Methods for performing content-based similarity analyses ofitems are well known in the art, and are therefore not described herein.

V-C Session Recommendations (FIGS. 8-12)

One limitation in the above-described service implementations is thatthey generally require users to purchase or rate products (InstantRecommendations embodiment), or place products into a shopping cart(Shopping Cart Recommendations embodiment), before personalrecommendations can be generated. As a result, the recommendationservice may fail to provide personal recommendations to a new visitor tothe site, even though the visitor has viewed many different items.Another limitation, particularly with the Shopping Cart Recommendationsembodiment, is that the service may fail to identify thesession-specific interests of a user who fails to place items into hisor her shopping cart.

In accordance with another aspect of the invention, these limitationsare overcome by providing a Session Recommendations service that storesa history or “click stream” of the products viewed by a user during thecurrent browsing session, and uses some or all of these products as theuser's “items of known interest” for purposes of recommending productsto the user during that browsing session. Preferably, the recommendedproducts are displayed on a personalized Web page (FIG. 11) thatprovides an option for the user to individually “deselect” the viewedproducts from which the recommendations have been derived. For example,once the user has viewed products A, B and C during a browsing session,the user can view a page listing recommended products derived bycombining the similar items lists for these three products. Whileviewing this personal recommendations page, the user can de-select oneof the three products to effectively remove it from the set of items ofknown interest, and then view recommendations derived from the remainingtwo products.

The click-stream data used to implement this service may optionallyincorporate product browsing activities over multiple Web sites. Forexample, when a user visits one merchant Web site followed by another,the two visits may be treated as a single “session” for purposes ofgenerating personal recommendations.

FIG. 8 illustrates the components that may be added to the system ofFIG. 1 to record real time session data reflecting product viewingevents, and to use this data to provide session-specific recommendationof the type shown in FIG. 11. Also shown are components for using thisdata to generate a viewing-history-based version of the similar itemstable 60, as described above section IV-B above.

As illustrated, the system includes an HTTP/XML application 37 thatmonitors clicks (page requests) of users, and records information aboutcertain types of events within a click stream table 39. The click streamtable is preferably stored in a cache memory 39 (volatile RAM) of aphysical server computer, and can therefore be rapidly and efficientlyaccessed by the Session Recommendations application 52 and other realtime personalization components. All accesses to the click stream table39 are preferably made through the HTTP/XML application, as shown. TheHTTP/XML application 37 may run on the same physical server machine(s)(not shown) as the Web server 32, or on a “service” layer of machinessitting behind the Web server machines. An important benefit of thisarchitecture is that it is highly scalable, allowing the click streamhistories of many thousands or millions of users to be maintainedsimultaneously.

In operation, each time a user views a product detail page, the Webserver 32 notifies the HTTP/XML application 37, causing the HTTP/XMLapplication to record the event in real time in a session-specificrecord of the click stream table. The HTTP/XML application may also beconfigured to record other click stream events. For example, when theuser runs a search for a product, the HTTP/XML application may recordthe search query, and/or some or all of the items displayed on theresulting search results page (e.g., the top X products listed).Similarly, when the user views a browse node page (a page correspondingto a node of a browse tree in which the items are arranged by category),the HTTP/XML application may record an identifier of the page or a listof products displayed on that page.

A user access to a search results page or a browse node page may, but ispreferably not, treated as a viewing event with respect to productsdisplayed on such pages. As discussed in sections VIII and XI below, thesession-specific histories of browse node accesses and searches may beused as independent or additional data sources for providingpersonalized recommendations.

In one embodiment, once the user has viewed a threshold number ofproduct detail pages (e.g., 1, 2 or 3) during the current session, theuser is presented with a link to a custom page of the type shown in FIG.11. The link includes an appropriate message such as “view the page youmade,” and is preferably displayed persistently as the user navigatesfrom page to page. When the user selects this link, a SessionRecommendations component 52 accesses the user's cached session recordto identify the products the user has viewed, and then uses some or allof these products as the “items of known interest” for generating thepersonal recommendations. These “Session Recommendations” areincorporated into the custom Web page (FIG. 11)—preferably along withother personalized content, as discussed below. The SessionRecommendations may additionally or alternatively be displayed on otherpages accessed by the user—either as explicit or implicitrecommendations.

The process for generating the Session Recommendations is preferably thesame as or similar to the process shown in FIG. 2, discussed above. Thesimilar items table 60 used for this purpose may, but need not, reflectviewing-history-based similarities. During the filtering portion of theFIG. 2 process (block 90), any recently viewed items may be filtered outof the recommendations list.

As depicted by the dashed arrow in FIG. 8, after a browsing session isdeemed to have ended, the session record (or a list of the productsrecorded therein) is moved to a query log database 42 so that it maysubsequently be used to generate a viewing-history-based version of thesimilar items table 60. As part of this process, two or more sessions ofthe same user may optionally be merged to form a multi-session productviewing history. For example, all sessions conducted by a user within aparticular time period (e.g., 3 days) may be merged. The product viewinghistories used to generate the similar items table 60 may alternativelybe generated independently of the click stream records, such as byextracting such data from a Web server access log. In one embodiment,the session records are stored anonymously (i.e., without anyinformation linking the records to corresponding users), such that userprivacy is maintained.

FIG. 9 illustrates the general form of the click stream table 39maintained in cache memory according to one embodiment of the invention.Each record in the click stream table corresponds to a particular userand browsing session, and includes the following information about thesession: a session ID, a list of IDs of product detail pages viewed, alist of page IDs of browse nodes viewed (i.e., nodes of a browse tree inwhich products are arranged by category), and a list of search queriessubmitted (and optionally the results of such search queries). The listof browse node pages and the list of search queries may alternatively beomitted. One such record is maintained for each “ongoing” session.

The browsing session ID can be any identifier that uniquely identifies abrowsing session. In one embodiment, the browsing session ID includes anumber representing the date and time at which a browsing sessionstarted. A “session” may be defined within the system based on timesbetween consecutive page accesses, whether the user viewed another Website, whether the user checked out, and/or other criteria reflectingwhether the user discontinued browsing.

Each page ID uniquely identifies a Web page, and may be in the form of aURL or an internal identification. For a product detail page (a pagethat predominantly displays information about one particular product),the product's unique identifier may be used as the page identification.The detail page list may therefore be in the form of the IDs of theproducts whose detail pages were viewed during the session. WherevoiceXML pages are used to permit browsing by telephone, a user accessto a voiceXML version of a product detail page may be treated as aproduct “viewing” event.

The search query list includes the terms and/or phrases submitted by theuser to a search engine of the Web site 30. The captured searchterms/phrases may be used for a variety of purposes, such as filteringor ranking the personal recommendations returned by the FIG. 2 process,and/or identifying additional items or item categories to recommend.

FIG. 10 illustrates one embodiment of a page-item table that mayoptionally be used to translate page IDs into corresponding product IDs.The page-item table includes a page identification field and a productidentification field. For purposes of illustration, productidentification fields of sample records in FIG. 10 are represented byproduct names, although a more compact identification may be used. Thefirst record of FIG. 10 represents a detail page (DP1) and itscorresponding product. The second record of FIG. 10 represents a browsenode page (BN1) and its corresponding list of products. A browse nodepage's corresponding list of products may include all of the productsthat are displayed on the browse node page, or a subset of theseproducts (e.g., the top selling or most-frequently viewed products).

In one embodiment, the process of converting page IDs to correspondingproduct IDs is handled by the Web server 32, which passes asession_ID/product_ID pair to the HTTP/XML application 37 in response tothe click stream event. This conversion task may alternatively behandled by the HTTP/XML application 37 each time a click stream event isrecorded, or may be performed by the Session Recommendations component52 when personal recommendations are generated.

FIG. 11 illustrates the general form of a personalized “page I made” Webpage according to a preferred embodiment. The page may be generateddynamically by the Session Recommendations component 52, or by a dynamicpage generation component (not shown) that calls the SessionRecommendations component. As illustrated, the page includes a list ofrecommended items 404, and a list of the recently viewed items 402 usedas the “items of known interest” for generating the list of recommendeditems. The recently viewed items 402 in the illustrated embodiment areitems for which the user has viewed corresponding product detail pagesduring the current session, as reflected within the user's currentsession record. As illustrated, each item in this list 402 may include ahyperlink to the corresponding detail page, allowing the user to easilyreturn to previously viewed detail pages.

As illustrated in FIG. 11, each recently-viewed item is displayedtogether with a check box to allow the user to individually deselect theitem. De-selection of an item causes the Session Recommendationscomponent 52 to effectively remove that item from the list of “items ofknown interest” for purposes of generating subsequent SessionRecommendations. A user may deselect an item if, for example, the useris not actually interested in the item (e.g., the item was viewed byanother person who shares the same computer). Once the user de-selectsone or more of the recently viewed items, the user can select the“update page” button to view a refined list of Session Recommendations404. When the user selects this button, the HTTP/XML application 37deletes the de-selected item(s) from the corresponding session record inthe click stream table 39, or marks such items as being deselected. TheSession Recommendations process 52 then regenerates the SessionRecommendations using the modified session record.

In another embodiment, the Web page of FIG. 11 includes an option forthe user to rate each recently viewed item on a scale of 1 to 5. Theresulting ratings are then used by the Session Recommendations component52 to weight the correspond similar items lists, as depicted in block 84of FIG. 2 and described above.

The “page I made” Web page may also include other types of personalizedcontent. For instance, in the example shown in FIG. 11, the page alsoincludes a list of top selling items 406 of a particular browse node.This browse node may be identified at page-rendering time by accessingthe session record to identify a browse node accessed by the user.Similar lists may be displayed for other browse nodes recently accessedby the user. The list of top sellers 406 may alternatively be derived byidentifying the top selling items within the product category orcategories to which the recently viewed items 402 correspond. Inaddition, the session history of browse node visits may be used togenerate personalized recommendations according to the method describedin section VIII below.

In embodiments that support browsing by voice, the customized Web pagemay be in the form of a voiceXML page, or a page according to anothervoice interface standard, that is adapted to be accessed by voice. Insuch embodiments, the various lists of items 402, 404, 406 may be outputto the customer using synthesized and/or pre-recorded voice.

An important aspect of the Session Recommendations service is that itprovides personalized recommendations that are based on the activitiesperformed by the user during the current session. As a result, therecommendations tend to strongly reflect the user's session-specificinterests. Another benefit is that the recommendations may be generatedand provided to users falling within one or both of the followingcategories: (a) users who have never made a purchase, rated an item, orplaced an item in a shopping cart while browsing the site, and (b) userswho are unknown to or unrecognized by the site (e.g., a new visitor tothe site). Another benefit is that the user can efficiently refine thesession data used to generate the recommendations.

The Session Recommendations may additionally or alternatively bedisplayed on other pages of the Web site 30. For example, the SessionRecommendations could be displayed when the user returns to the homepage, or when the user views the shopping cart. Further, the SessionRecommendations may be presented as implicit recommendations, withoutany indication of how they were generated.

VI. Display of Recently Viewed Items

As described above with reference to FIG. 11, the customized Web pagepreferably includes a hypertextual list 402 of recently viewed items(and more specifically, products whose detail pages were visited induring the current session). This feature may be implementedindependently of the Session Recommendation service as a mechanism tohelp users locate the products or other items they've recently viewed.For example, as the user browses the site, a persistent link may bedisplayed which reads “view a list of the products you've recentlyviewed.” A list of the recently viewed items may additionally oralternatively be incorporated into some or all of the pages the userviews.

In one embodiment, each hyperlink within the list 402 is to a productdetail page visited during the current browsing session. This list isgenerated by reading the user's session record in the click stream table39, as described above. In other embodiments, the list of recentlyviewed items may include detail pages viewed during prior sessions(e.g., all sessions over last three days), and may include links torecently accessed browse node pages and/or recently used search queries.

Further, a filtered version of a user's product viewing history may bedisplayed in certain circumstances. For example, when a user views aproduct detail page of an item in a particular product category, thisdetail page may be supplemented with a list of (or a link to a list of)other products recently viewed by the user that fall within the sameproduct category. For instance, the detail page for an MP3 player mayinclude a list of any other MP3 players, or of any other electronicsproducts, the user has recently viewed.

An important benefit of this feature is that it allows users to moreeasily comparison shop.

VII. Display of Related Items on Product Detail Pages (FIGS. 12 and 13)

In addition to using the similar items table 60 to generate personalrecommendations, the table 60 may be used to display “canned” lists ofrelated items on product detail pages of the “popular” items (i.e.,items for which a similar items list 64 exists). FIG. 12 illustratesthis feature in example form. In this example, the detail page of aproduct is supplemented with the message “customers who viewed this itemalso viewed the following items,” followed by a hypertextual list 500 offour related items. In this particular embodiment, the list is generatedfrom the viewing-history-based version of the similar items table(generated as described in section IV-B).

An important benefit to using a similar items table 60 that reflectsviewing-history-based similarities, as opposed to a table based purelyon purchase histories, is that the number of product viewing events willtypically far exceed the number of product purchase events. As a result,related items lists can be displayed for a wider selection ofproducts—including products for which little or no sales data exists. Inaddition, for the reasons set forth above, the related items displayedare likely to include items that are substitutes for the displayed item.

FIG. 13 illustrates a process that may be used to generate a relateditems list 500 of the type shown in FIG. 12. As illustrated, the relateditems list 500 for a given product is generated by retrieving thecorresponding similar items list 64 (preferably from aviewing-history-based similar items table 60 as described above),optionally filtering out items falling outside the product category ofthe product, and then extracting the N top-rank items. Once this relateditems list 64 has been generated for a particular product, it may bere-used (e.g., cached) until the relevant similar items table 60 isregenerated.

VIII. Recommendations Based on Browse Node Visits

As indicated above and shown in FIG. 9, a history of each user's visitsto browse node pages (generally “browse nodes”) may be stored in theuser's session record. In one embodiment, this history of viewed browsenodes is used independently of the user's product viewing history toprovide personalized recommendations.

For example, in one embodiment, the Session Recommendations process 52identifies items that fall within one or more browse nodes viewed by theuser during the current session, and recommends some or all of theseitems to the user (implicitly or explicitly) during the same session. Ifthe user has viewed multiple browse nodes, greater weight may be givento an item that falls within more than one of these browse nodes,increasing the item's likelihood of selection. For example, if the userviews the browse node pages of two music categories at the same level ofthe browse tree, a music title falling within both of thesenodes/categories would be selected to recommend over a music titlefalling in only one.

As with the session recommendations based on recently viewed products,the session recommendations based on recently viewed browse nodes may bedisplayed on a customized page that allows the user to individuallydeselect the browse nodes and then update the page. The customized pagemay be the same page used to display the product viewing history basedrecommendations (FIG. 11).

A hybrid of this method and the product viewing history based method mayalso be used to generate personalized recommendations.

IX. Recommendations Based on Recent Searches

Each user's history of recent searches, as reflected within the sessionrecord, may be used to generate recommendations in an analogous mannerto that described in section VIII. The results of each search (i.e., thelist of matching items) may be retained in cache memory to facilitatethis task.

In one embodiment, the Session Recommendations component 52 identifiesitems that fall within one or more results lists of searches conductedby the user during the current session, and recommends some or all ofthese items to the user (implicitly or explicitly) during the samesession. If the user has conducted multiple searches, greater weight maybe given to an item falling within more than one of these search resultslists, increasing the item's likelihood of selection. For example, ifthe user conducts two searches, a music title falling within both setsof search results would be selected to recommend over a music titlefalling in only one.

As with the session recommendations based on recently viewed products,the session recommendations based on recently conducted searches may bedisplayed on a customized page that allows the user to individuallydeselect the search queries and then update the page. The customizedpage may be the same page used to display the product viewing historybased recommendations (FIG. 11) and/or the browse node basedrecommendations (section VIII).

Any appropriate hybrid of this method, the product viewing history basedmethod (section V-C), and the browse node based method (section VIII),may be used to generate personalized recommendations.

X. Recommendations Within Physical Stores

The recommendation methods described above can also be used to providepersonalized recommendations within physical stores. For example, eachtime a customer checks out at a grocery or other physical store, a listof the purchased items may be stored. These purchase lists may then beused to periodically generate a similar items table 60 using the processof FIG. 3A or 3B. Further, where a mechanism exists for associating eachpurchase list with the customer (e.g., using club cards), the purchaselists of like customers may be combined such that the similar itemstable 60 may be based on more comprehensive purchase histories.

Once a similar items table has been generated, a process of the typeshown in FIG. 2 may be used to provide discount coupons or other typesof item-specific promotions at check out time. For example, when a userchecks out at a cash register, the items purchased may be used as the“items of known interest” in FIG. 2, and the resulting list ofrecommended items may be used to select from a database of coupons ofthe type commonly printed on the backs of grocery store receipts. Thefunctions of storing purchase lists and generating personalrecommendations may be embodied within software executed by commerciallyavailable cash register systems.

Although this invention has been described in terms of certain preferredembodiments, other embodiments that are apparent to those of ordinaryskill in the art, including embodiments that do not provide all of thefeatures and benefits set forth herein, are also within the scope ofthis invention. Accordingly, the scope of the present invention isintended to be defined only by reference to the appended claims.

In the claims which follow, any reference characters used to denoteprocess steps are provided for convenience of description only, and notto imply a particular order for performing the steps.

1. A data mining method, comprising: by a computer system that comprisesone or more machines: maintaining, in computer storage, session recordsof a plurality of users of an electronic catalog of items, said sessionrecords identifying catalog items selected by users for viewing withincorresponding user sessions, said session records maintained by saidcomputer system without requiring the users to explicitly create listsof items, wherein maintaining said session records comprises monitoringuser accesses to item detail pages, each of which predominantly containsinformation about one particular catalog item, to thereby identifyparticular catalog items selected by users for viewing in saidelectronic catalog; programmatically analyzing the session records togenerate data values reflective of item co-occurrences within thesession records, each data value corresponding to a respective pair ofcatalog items and representing a strength of a relationship between thetwo catalog items of said pair, each data value being dependent upon anumber of said user sessions in which the item detail paces of bothcatalog items of the respective pair were accessed; generating a datastructure that associates particular catalog items with correspondingsets of related catalog items, wherein the data structure is generatedbased, at least in-part, on said data values; and for each of aplurality of catalog items, using said data structure to supplement acorresponding item detail pace of the electronic catalog with anotification of other catalog items that are viewed by users who viewthe respective catalog item.
 2. The method of claim 1, wherein theelectronic catalog is arranged such that each user access to an itemdetail page generally represents an affirmative request by the user forinformation regarding a corresponding catalog item.
 3. The method ofclaim 1, wherein analyzing the session records comprises determining,for an item pair consisting of a first catalog item and a second catalogitem, a number of sessions in which respective item detail pages of thefirst and the second catalog items were both accessed.
 4. The method ofclaim 1, wherein the catalog items are products represented in theelectronic catalog.
 5. The method of claim 1, wherein generating thedata structure comprises analyzing the data values to determine whichcatalog items are to be mapped to each other in the data structure. 6.The method of claim 5, wherein generating the data structure furthercomprises storing the data values associated with the selected pairs ofcatalog items in the data structure.
 7. The method of claim 1, furthercomprising, via execution of instructions by said computer system, usingthe data structure to generate personalized item recommendations foreach of a plurality of target users.
 8. The method of claim 1, furthercomprising, via execution of instructions by said computer system, usingthe data structure, in combination with a record of a plurality ofcatalog items viewed by a target user during a current session, togenerate personalized, session-specific item recommendations for thetarget user.
 9. The method of claim 1, wherein the method comprisessupplementing an item detail page for a first catalog item with a listof additional catalog items, and with an indication that the additionalcatalog items in said list are viewed by users who view the firstcatalog item, said list being based on the data structure.
 10. Themethod of claim 1, wherein each user session is a period ofsubstantially continuous browsing activity by corresponding user, andeach session record corresponds uniquely to a respective user session,said browsing activity being limited to actions performed duringbrowsing of the electronic catalog.
 11. The method of claim 1, whereinmaintaining said session records comprises the computer systemdetermining whether a particular session has ended based at least partlyon whether a checkout transaction has occurred.
 12. The method of claim1, wherein the method comprises treating a user access to an item detailpage of said electronic catalog as a selection of a correspondingcatalog item for viewing.
 13. The method of claim 1, whereinprogrammatically analyzing the session records comprises determining adistance between two catalog item viewing events within a session, andtaking said distance into consideration in generating a data value for acorresponding pair of catalog items.
 14. The method of claim 13, whereindetermining said distance comprises determining a number of pageaccesses that occurred between the two catalog item viewing events. 15.The method of claim 13, wherein determining said distance comprisesdetermining an amount of time between the two catalog item viewingevents.
 16. The method of claim 1, wherein the computer system is a website system that comprises multiple machines.
 17. A data mining system,comprising: a computer system that comprises one or more machines, saidcomputer system programmed, via executable code stored in computerstorage, to perform a method that comprises: maintaining, in computerstorage, session records of a plurality of users of an electroniccatalog of items, said session records identifying catalog itemsselected by users for viewing within corresponding user sessions, saidsession records maintained by said computer system without requiring theusers to explicitly create lists of items, wherein maintaining saidsession records comprises monitoring user accesses to item detail pages,each of which predominantly contains information about one particularcatalog item, to thereby identify particular catalog items selected byusers for viewing in the electronic catalog; analyzing the sessionrecords to generate data values reflective of item co-occurrences withinthe session records, each data value corresponding to a respective pairof catalog items and representing a strength of a relationship betweenthe two catalog items of said pair, each data value being dependent upona number of said user sessions in which the item detail pages of bothcatalog items of the respective pair were accessed; generating a datastructure that associates particular catalog items with correspondingsets of related catalog items, wherein the data structure is generatedbased, at least in-part, on said data values; and for each of aplurality of catalog items, using said data structure to supplement acorresponding item detail page of the electronic catalog with anotification of other catalog items that are viewed by users who viewthe respective catalog item.
 18. The data mining system of claim 17,wherein the computer system is programmed to analyze the session recordsin part by determining, for an item pair consisting of a first catalogitem and a second catalog item, a number of sessions in which respectiveitem detail pages of the first and second catalog items were bothaccessed.
 19. The data mining system of claim 17, wherein the catalogitems are products represented in the electronic catalog.
 20. The datamining system of claim 17, wherein the computer system is programmed touse the data values to determine which catalog items are to be mapped toeach other in the data structure.
 21. The data mining system of claim17, wherein the computer system is additionally programmed to use thedata structure to generate personalized item recommendations for each ofa plurality of target users.
 22. The data mining system of claim 17,wherein the computer system is additionally programmed to use the datastructure, in combination with a record of a plurality of catalog itemsviewed by a target user during a current session, to generatepersonalized, session-specific item recommendations for the target user.23. The data mining system of claim 17, wherein the electronic catalogis arranged such that each user access to an item detail page generallyrepresents an affirmative request by the user for information regardinga corresponding catalog item.
 24. The data mining system of claim 17,wherein each session record represents a respective period ofsubstantially continuous browsing activity by a user, said browsingactivity being limited to actions performed during browsing of theelectronic catalog.
 25. The data mining system of claim 17, wherein thecomputer system is programmed to determine, in generating said sessionrecords, whether a particular session has ended based at least partly onwhether a checkout transaction has occurred.
 26. The data mining systemof claim 17, wherein the computer system is programmed to (1) use thedata structure to supplement an item detail page for a first catalogitem with a list of additional catalog items, and (2) provide anotification that the catalog items in said list are viewed by users whoview the first catalog item.
 27. The data mining system of claim 17,wherein the computer system is programmed to measure a distance betweentwo catalog item viewing events within a session, and to take saiddistance into consideration in generating a data value for acorresponding pair of catalog items.
 28. The data mining system of claim27, wherein the computer system is programmed to determine said distancebased at least partly on a number of page accesses that occur betweenthe two catalog item viewing events.
 29. The data mining system of claim27, wherein the computer system is programmed to determine said distancebased at least partly on an amount of time than transpires between thetwo catalog item viewing events.
 30. Physical computer storage whichstores executable code that, when executed by a computer system, causesthe computer system to perform a method that comprises: maintaining, incomputer storage, session records of a plurality of users of anelectronic catalog of items, said session records identifying catalogitems selected by users for viewing within corresponding user sessions,said session records maintained without requiring the users toexplicitly create lists of items, wherein maintaining said sessionrecords comprises monitoring user accesses to item detail pages, each ofwhich predominantly contains information about one particular catalogitem, to thereby identify particular catalog items selected by users forviewing; analyzing the session records to generate data valuesreflective of item co-occurrences within the session records, each datavalue corresponding to a respective pair of catalog items andrepresenting a strength of a relationship between the two catalog itemsof said pair, each data value being dependent upon a number of said usersessions in which the respective item detail pages of both catalog itemsof the respective pair were accessed; generating a data structure thatassociates particular catalog items with corresponding sets of relatedcatalog items, wherein the data structure is generated based, at leastin-part, on said data values; and for each of a plurality of catalogitems, using said data structure to supplement a corresponding itemdetail page of the electronic catalog with a notification of othercatalog items that are viewed by users who view the respective catalogitem.
 31. The physical computer storage of claim 30, wherein thephysical computer storage additionally stores executable code that iscapable of causing the computer system to use the data structure, incombination with a record of a plurality of catalog items viewed by atarget user during a current session, to generate personalized,session-specific item recommendations for the target user.
 32. Thephysical computer storage of claim 30, wherein the electronic catalog isarranged such that each user access to an item detail page generallyrepresents an affirmative request by the user for information regardinga corresponding catalog item.
 33. The physical computer storage of claim30, wherein the physical computer storage additionally stores executablecode that is capable of causing the computer system to determine, ingenerating said session records, whether a particular session has endedbased at least partly on whether a checkout transaction has occurred.34. The physical computer storage of claim 30, wherein the methodcomprises supplementing an item detail page for a first catalog itemwith a list of additional catalog items, and with an indication that thecatalog items in said list are viewed by users who view the firstcatalog item, said list being based on the data structure.
 35. Thephysical computer storage of claim 30, wherein the session records aremaintained in computer storage without any information that links thesession records to corresponding users, whereby user privacy ismaintained.
 36. The method of claim 1, wherein maintaining the sessionrecords comprises storing the session records in said computer storagewithout any information linking the session records to correspondingusers, whereby user privacy is maintained.
 37. The data mining system ofclaim 17, wherein the computer system is programmed to store and analyzethe session records anonymously.